Missing data is common in a large number of applications, such as in network traffic data, in medical data, and in economic data, to name a few. Data may be missing for any number of reasons. In one example, devices configured to record data may be non-operational during power-related emergencies or these devices may be powered down during scheduled maintenance, resulting in missing data that would otherwise be recorded. In addition, data may be recorded but then lost for any number of reasons, such as dropped data packets when network congestion levels are high. Furthermore, recorded data may be inaccurate, such that the some of the data that is recorded may not reveal much information regarding the true value.
Often, it is desirable to replace the missing (or inaccurate) data in a signal with predictive model estimates. Various algorithms using the non-missing data are often used to derive estimates of the missing data and vary in complexity. For example, interpolation methods range from simple linear interpolation processes to nth degree polynomial interpolation. Data replacement algorithms may also use model estimates based on some knowledge of the signal to replace missing data. It is desirable to use a suitable process to replace missing data in a signal, but different processes may be suitable under different circumstances.
Systems and methods to replace missing data in a signal would therefore be of great benefit in data analysis.